Unsupervised interslice super-resolution for medical images

ABSTRACT

An unsupervised machine learning method with self-supervision losses improves a slice-wise spatial resolution of 3D medical images with thick slices, and does not require high resolution images as the ground truth for training. The method utilizes information from high-resolution dimensions to increase a resolution of another desired dimension.

BACKGROUND

The aspects of the present disclosure relate generally to 3D medicalimaging, and in particular to using deep learning to improve theresolution of 3D medical images.

3D medical images, for example, magnetic resonance imaging, computedtomography, and microscopy, are generally acquired with relatively largedimensions and resulting low resolution in the interslice direction dueto limitations of acquisition time and equipment in clinicalenvironments. Given these relatively large dimensions and lowresolution, downstream applications such as 3D rendering for betterimage comprehension and quantitative analysis, for example for analysisof certain anatomical structure volumes, usually provide unsatisfactoryresults.

SUMMARY

It would be advantageous to provide a method and system that providesincreased resolution in the interslice direction without humanintervention.

The disclosed embodiments are directed to an unsupervised machinelearning method and system with self-supervision losses that improves aslice-wise spatial resolution of 3D medical images with thick slices,and does not require high resolution images as the ground truth fortraining. The model utilizes information from high-resolution dimensionsto increase a resolution of another desired dimension.

In another aspect, the disclosed embodiments are directed to a methodincluding acquiring first 3D image slices with interslice resolutionslower than intraslice resolutions, upsampling the first 3D image slicesto produce second 3D image slices with increased interslice resolutionsby using an adjustable mathematical mapping, downsampling the second 3Dimage slices, calculating first self-supervision losses by measuringdifferences between the downsampled image slices and the first 3D imageslices, modifying the mathematical mapping to minimize the firstself-supervision losses, using the modified mathematical mapping tomodify the second 3D image slices; and providing the modified second 3Dimage slices to a user through a user interface.

The method may include downsampling the modified second 3D image slicesto dimensions of the first 3D image slices.

The method may include switching dimensions of the modified second 3Dimage slices to produce third 3D image slices, downsampling the third 3Dimage slices to dimensions of the first 3D image slices, upsampling thedownsampled third 3D image slices to produce fourth 3D image slicesusing the adjustable mathematical mapping, calculating secondself-supervision losses by measuring differences between the third andfourth 3D image slices, and adjusting the adjustable mathematicalmapping to minimize the second self-supervision losses.

Switching dimensions of the second 3D image slices may produce the third3D image slices with interslice resolutions matching the intrasliceresolutions of the first 3D images.

Upsampling the downsampled third 3D image slices may produce the fourth3D image slices with dimensions of the third 3D image slices.

The method may still further include switching dimensions of the fourth3D image slices to produce fifth 3D image slices, downsampling the fifth3D image slices to produce sixth 3D image slices, calculating thirdself-supervision losses by measuring differences between the sixth 3Dimage slices and the first 3D image slices, adjusting the mapping tominimize the second and third self-supervision losses, using theoptimized mapping to generate second 3D image slices from the first 3Dimage slices; and providing the second 3D image slices to a user througha user interface.

Switching dimensions of the fifth 3D image slices may produce the sixth3D image slices with intraslice and interslice axes matching intrasliceand interslice axes of the first 3D image slices.

Downsampling the fifth 3D image slices may produce the sixth 3D imageslices with dimensions of the first 3D image slices.

The method may further include using a deep neural network to formulatethe mathematical mapping for upsampling the first 3D image slices to thesecond 3D image slices and a computational framework to optimize thedeep neural network by switching dimensions of the second and fourthimage slices, downsampling the third and the fifth image slices,computing the first, second, and third self-supervision losses, andadjusting the deep neural network to minimize the losses.

The deep neural network may include one or more gated recurrent units,long short term memory networks, fully convolutional neural networkmodels, generative adversarial networks, back propagation neural networkmodels, radial basis function neural network models, deep belief netsneural network models, and Elman neural network models.

In another aspect the disclosed embodiments are directed to a systemincluding a source of first 3D image slices with interslice resolutionslower than intraslice resolutions, a deep neural network to upsample thefirst 3D image slices to the second 3D image slices and upsample thedownsampled third 3D image slices to the fourth 3D image slices, and acomputational framework to optimize the deep neural network by switchingdimensions of the second and fourth image slices, downsampling the thirdand the fifth image slices, computing the first, second, and thirdself-supervision losses, and adjusting the deep neural network tominimize the losses, and the system may further include a user interfacefor providing the second 3D image slices produced by the optimized deepneural network to a user.

These and other aspects, implementation forms, and advantages of theexemplary embodiments will become apparent from the embodimentsdescribed herein considered in conjunction with the accompanyingdrawings. It is to be understood, however, that the description anddrawings are designed solely for purposes of illustration and not as adefinition of the limits of the disclosed invention, for which referenceshould be made to the appended claims. Additional aspects and advantagesof the invention will be set forth in the description that follows, andin part will be obvious from the description, or may be learned bypractice of the invention. Moreover, the aspects and advantages of theinvention may be realized and obtained by means of the instrumentalitiesand combinations particularly pointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following detailed portion of the present disclosure, theinvention will be explained in more detail with reference to the exampleembodiments shown in the drawings. These embodiments are non-limitingexemplary embodiments, in which like reference numerals representsimilar structures throughout the several views of the drawings,wherein:

FIG. 1 illustrates a general overview of a workflow 100 for generatinginterslice super-resolution for medical images with unsupervised deeplearning;

FIG. 2 illustrates a schematic block diagram of an exemplary systemincorporating aspects of the disclosed embodiments;

FIG. 3 illustrates an exemplary architecture of a computing engine thatmay be used to implement the disclosed embodiments;

FIG. 4 depicts an exemplary simple deep learning model that may beutilized to implement the disclosed embodiments; and

FIG. 5 shows a detailed flow diagram of a process for providingunsupervised interslice super resolution medical images.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirits andscope of the present disclosure. Thus, the present disclosure is notlimited to the embodiments shown, but to be accorded the widest scopeconsistent with the claims.

It will be understood that the term “system,” “unit,” “module,” and/or“block” used herein are one method to distinguish different components,elements, parts, section or assembly of different level in ascendingorder. However, the terms may be displaced by other expression if theymay achieve the same purpose.

It will be understood that when a unit, module or block is referred toas being “on,” “connected to” or “coupled to” another unit, module, orblock, it may be directly on, connected or coupled to the other unit,module, or block, or intervening unit, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices may be provided on a computer-readable medium, such asa compact disc, a digital video disc, a flash drive, a magnetic disc, orany other tangible medium, or as a digital download (and can beoriginally stored in a compressed or installable format that needsinstallation, decompression, or decryption prior to execution). Suchsoftware code may be stored, partially or fully, on a storage device ofthe executing computing device, for execution by the computing device.Software instructions may be embedded in firmware, such as an ErasableProgrammable Read Only Memory (EPROM). It will be further appreciatedthat hardware modules/units/blocks may be included in connected logiccomponents, such as gates and flip-flops, and/or can be included ofprogrammable units, such as programmable gate arrays or processors. Themodules/units/blocks or computing device functionality described hereinmay be implemented as software modules/units/blocks, but may berepresented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include,”and/or “comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The disclosed embodiments are directed to a method and system thatutilize deep learning to recover isotropic images from images withrelatively large interslice dimensions, which are able to generate sharppatterns in the interslice direction and improve the quality ofdownstream tasks with little or no human intervention.

It may be possible to implement deep learning methods that utilizesupervised learning to improve the resolution of 3D medical images,where a deep learning model learns to generate high resolution imagesusing pairs of low resolution images for training. However, generatinghigh resolution images for training is costly, and in some circumstancesmay even be infeasible. The disclosed embodiments are directed to a deeplearning method utilizing unsupervised learning for increasinginterslice image resolution which does not require high resolutionimages as a training ground truth.

For purposes of the disclosed embodiments, the acquired 3D medicalimages have intraslice X and Y dimensions with relatively highresolution and interslice Z dimensions with a resolution lower than theX and Y dimensions, and the disclosed embodiments are directed toincreasing the resolution of the interslice Z dimensions, also referredto as achieving super-resolution. While the disclosed embodiments aredescribed in the context of 3D medical image slices, it should beunderstood that the disclosed embodiments may utilize any suitable 3Dimages. Due to the difficulty of acquiring ground truth high-resolutionimages, the disclosed embodiments utilize an unsupervised trainingstrategy, where the deep learning model is trained on low-resolutiondatasets with self-supervision losses without human intervention.

Various operations of the system and method for achievingsuper-resolution are described in the context of utilizing a deeplearning model, and it should be understood that individual deeplearning models may be utilized for various operations, differentdeep-learning models may be used for combinations of various operations,or a single deep learning model may be utilized for all the operations.

FIG. 1 illustrates a general overview of a workflow 100 for generatinginterslice super-resolution for medical images with unsupervised deeplearning. Original image slices I_(LR) with intraslice X and Ydimensions and interslice Z dimensions may be acquired, and a deepneural network f_(θ) with parameters θ that increases the resolution ofthe image slices in the Z dimension may be initialized at block 105. Theincreased resolution image slices I_(HR) may be downsampled to theoriginal image slice dimensions, and the downsampled image slices

may be compared with the original image slices I_(LR) to calculate firstself-supervision losses L₁ at block 110:

L ₁ =m(

,I _(LR))

=ds(f _(θ)(I _(LR)))

, where m is a metric measuring the difference between

and I_(LR) and ds is a downsampling operation.

The dimensions of the increased resolution image slices may be switchedto produce image slices with interslice dimensions in the X dimensionsS_(HR,X) and to produce image slices with interslice dimensions in the Ydimensions S_(HR,Y), the switched dimension increased resolution imageslices may be downsampled to the original image slice sizes in therespective X and Y dimensions

, the resolution of the downsampled switched dimension image slices maybe increased to the resolutions of the switched dimension increasedresolution image slices by the deep neural network f_(θ), and may becompared with the switched dimension increased resolution slices S_(HR)to calculate second self-supervision losses L₂ at block 115:

L ₂ =m(S′ _(HR) ,S _(HR))

S′ _(HR) =f _(θ)(

)   (2)

The dimensions of the switched dimension increased resolution imageslices S′_(HR) may be switched back to intraslice X and Y dimensions andinterslice Z dimensions S″_(HR), the switched back increased resolutionimage slices may be downsampled, and may be compared with the originalimage slices I_(LR) to calculate third self-supervision losses L₃ atblock 120:

L ₃ =m(ds(S″ _(HR)),I _(LR))   (3)

The deep neural network may be updated iteratively to minimize theself-supervision losses (Eq. 1-3) that serve as constraints under whichthe network learns a mapping from low resolution image slices to highresolution image slices with consistent features as in the lowresolution image slices at block 125.

The deep neural network may be used to provide image slices of highinter-slice resolution with low resolution image slices as inputs afterbeing optimized to minimize the self-supervision losses (Eq. 1-3) atblock 130.

FIG. 2 illustrates a schematic block diagram of an exemplary system 200incorporating aspects of the disclosed embodiments. The system 200 mayinclude a source of 3D medical image slices 202 with interslice Zdimensions having a lower resolution than intraslice X and Y dimensions,at least one deep learning model 204 for performing the operationsdisclosed herein, and one or more user interfaces, or other outputdevices 206 for providing the super-resolution image slices to one ormore users. It should be understood that the components of the system200 may be implemented in hardware, software, or a combination ofhardware and software.

FIG. 3 illustrates an exemplary architecture of a computing engine 300that may be used to implement the disclosed embodiments. The computingengine 300 may include computer readable program code stored on at leastone computer readable medium 302 for carrying out and executing theprocess steps described herein. The computer readable program code forcarrying out operations for aspects of the present disclosure may bewritten in any combination of one or more programming languages,including an object-oriented programming language such as Java, Scala,Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET, Python or the like,conventional procedural programming languages, such as the “C”programming language, Visual Basic, Fortran 2103, Perl, COBOL 2102, PHP,ABAP, dynamic programming languages such as Python, Ruby, and Groovy, orother programming languages. The computer readable program code mayexecute entirely on the computing engine 300, partly on the computingengine 300, as a stand-alone software package, or partly or entirely ona remote computer or server, such as a cloud service.

The computer readable medium 302 may be a memory of the computing engine300. In alternate aspects, the computer readable program code may bestored in a memory external to, or remote from, the computing engine300. The memory may include magnetic media, semiconductor media, opticalmedia, or any media which is readable and executable by a computer. Thecomputing engine 300 may also include a computer processor 304 forexecuting the computer readable program code stored on the at least onecomputer readable medium 302. In at least one aspect, the computingengine 300 may include one or more input or output devices, generallyreferred to as a user interface 306 which may operate to allow input tothe computing engine 300 or to provide output from the computing engine300, respectively. The computing engine 300 may be implemented inhardware, software or a combination of hardware and software.

The computing engine 300 may generally operate to support one or moredeep learning models. FIG. 4 depicts an exemplary simple deep learningmodel 400 that may be utilized to implement the disclosed embodiments.While a simple deep learning model is shown, it should be understoodthat the disclosed embodiments may be implemented utilizing a deeplearning model including one or more gated recurrent units (GRUs), longshort term memory (LSTM) networks, fully convolutional neural networks(FCNs), generative adversarial networks (GANs), back propagation (BP)neural networks, radial basis function (RBF) neural networks, deepbelief nets (DBN) neural networks, Elman neural networks, attentionneural networks, or any deep learning model capable of performing theoperations described herein. It should also be understood that thedifferent functions of the disclosed embodiments may be implemented witha deep learning model with parameters shared among the differentoperations performed by the deep learning model.

The deep learning model 400 may be trained to map low resolution to highresolution image slices with self supervision by ensuring a consistencybetween the original and downsampled image slices, between the switcheddimension increased resolution image slices and the increased resolutiondownsampled switched dimension image slices, and between the downsampledswitched back increased resolution image slices and the original imageslices.

The computing engine 300 may also provide a platform for thecomputational framework. For example, the computational framework mayinclude a combination of software, hardware, processors, and memory thatmay operate to manipulate and process image slices as described hereinunder control of the computer readable program code, and may beintegrated within the hardware and software of the computing engine 300.

FIG. 5 shows a detailed flow diagram of the computational framework 500for optimizing the deep neural network with self-supervised learningwhere it is trained to map an interslice low resolution medical image toan interslice high resolution medical image under self-supervisionconstraints, according to the disclosed embodiments. A plurality of lowresolution (LR) image slices 502, defined as having Z dimensions in theinterslice direction with lower resolutions than X and Y dimensions inthe intraslice directions, are provided to a deep learning model,referred to here as a network 504. The network 504 operates to up samplethe LR image slices 502 to produce image slices with an increasedresolution in the Z dimension 506, by using an adjustable mathematicalmapping. The image slices with an increased resolution in the Zdimension 506 are downsampled in the Z dimension to produce LRdownsampled image slices 508 with the same size as the LR image slices502. The computational framework calculates first self-supervisionlosses 510 by measuring differences between the downsampled image slices508 and the LR image slices 502. The network 504 learns to map the LRimage slices 502 to the increased resolution image slices 506 byextracting features in the LR image slices 502 and modifying themathematical mapping to minimize the first self-supervision losses 510and uses the modified mathematical mapping to modify the image sliceswith an increased resolution in the Z dimension 506.

The dimensions of the modified image slices with an increased resolutionin the Z dimension 506 are rotated or switched to synthesize imageslices with an increased resolution in the Z dimension with aninterslice direction in the Y dimension 512 and synthesize image sliceswith an increased resolution in the Z dimension with an interslicedirection in the X dimension 514. The image slices with an interslicedirection in the Y dimension 512 are downsampled in the Y dimension toproduce LR image slices with an interslice direction in the Y dimension516, and LR image slices with an interslice direction in the X dimension518. The LR image slices with an interslice direction in the Y dimension516, and the LR image slices with an interslice direction in the Xdimension 518 are upsampled to produce HR image slices with aninterslice direction in the Y dimension 520, and HR image slices with aninterslice direction in the X dimension 522. The network 504 learns tomap the downsampled resolution increased image slices 516, 518 to theresolution increased image slices 512, 514 by extracting features in thedownsampled resolution increased image slices 516, 518 and minimizingthe second self-supervision losses 524, 526 that measure the differencebetween its output 520, 522 and the resolution increased image slices512, 514.

The dimensions of the HR image slices with an interslice direction inthe Y dimension 520 are rotated or switched to an orientation withintraslice X and Y dimensions and interslice Z dimensions 528, and theresulting image slices are downsampled to produce LR downsampled imageslices 532 with the same size as the LR image slices 502. The network504 learns to minimize the third self-supervision losses 536, 538 thatmeasure differences between the LR image slices 532, 534 and LR imageslices 502.

It should be noted that deep learning model parameters adjusted as aresult of the feature learning and the self-supervision lossminimizations are shared 540 across the different operations performedby the network 504.

It also be noted that in the event that high resolution images areavailable as a training ground truth, for example, from a different kindof modality, it should be understood that low resolution images may besynthesized by downsampling in the Z dimension and using thecorresponding high and low resolution images as training pairs topre-train the network 504.

Thus, while there have been shown, described and pointed out,fundamental novel features of the invention as applied to the exemplaryembodiments thereof, it will be understood that various omissions,substitutions and changes in the form and details of devices and methodsillustrated, and in their operation, may be made by those skilled in theart without departing from the spirit and scope of the presentlydisclosed invention. Further, it is expressly intended that allcombinations of those elements, which perform substantially the samefunction in substantially the same way to achieve the same results, arewithin the scope of the invention. Moreover, it should be recognizedthat structures and/or elements shown and/or described in connectionwith any disclosed form or embodiment of the invention may beincorporated in any other disclosed or described or suggested form orembodiment as a general matter of design choice. It is the intention,therefore, to be limited only as indicated by the scope of the claimsappended hereto.

What is claimed is:
 1. A method comprising: acquiring first 3D imageslices with interslice resolutions lower than intraslice resolutions;upsampling the first 3D image slices to produce second 3D image sliceswith increased interslice resolutions by using an adjustablemathematical mapping; downsampling the second 3D image slices;calculating first self-supervision losses by measuring differencesbetween the downsampled image slices and the first 3D image slices;modifying the mathematical mapping to minimize the firstself-supervision losses; using the modified mathematical mapping tomodify the second 3D image slices; and providing the modified second 3Dimage slices to a user through a user interface.
 2. The method of claim1, further comprising downsampling the modified second 3D image slicesto dimensions of the first 3D image slices.
 3. The method of claim 1,further comprising: switching dimensions of the modified second 3D imageslices to produce third 3D image slices; downsampling the third 3D imageslices to dimensions of the first 3D image slices; upsampling thedownsampled third 3D image slices to produce fourth 3D image slicesusing the adjustable mathematical mapping; calculating secondself-supervision losses by measuring differences between the fourth andthird 3D image slices; and adjusting the adjustable mathematical mappingto minimize the second self-supervision losses.
 4. The method of claim3, wherein switching dimensions of the second 3D image slices producesthe third 3D image slices with interslice resolutions matching theintraslice resolutions of the first 3D image slices.
 5. The method ofclaim 3, wherein upsampling the downsampled third 3D image slicesproduces the fourth 3D image slices with dimensions of the third 3Dimage slices.
 6. The method of claim 3, further comprising: switchingdimensions of the fourth 3D image slices to produce fifth 3D imageslices; downsampling the fifth 3D image slices to produce sixth 3D imageslices; calculating third self-supervision losses by measuringdifferences between the sixth 3D image slices and the first 3D imageslices; and adjusting the mapping to minimize the third self-supervisionlosses.
 7. The method of claim 6, wherein switching dimensions of thefourth 3D image slices produces the fifth 3D image slices withintraslice and interslice axes matching intraslice and interslice axesof the first 3D image slices.
 8. The method of claim 6, whereindownsampling the fifth 3D image slices produces the sixth 3D imageslices with dimensions of the first 3D image slices.
 9. The method ofclaim 1, further comprising using a deep neural network to formulate themathematical mapping for reconstructing the second 3D image slices fromthe first 3D image slices and reconstructing the fourth 3D image slicesfrom the downsampled third 3D image slices.
 10. The method of claim 9,wherein the deep neural network comprises one or more gated recurrentunits, long short term memory networks, fully convolutional neuralnetwork models, generative adversarial networks, back propagation neuralnetwork models, radial basis function neural network models, deep beliefnets neural network models, and Elman neural network models.
 11. Asystem comprising: a source of first 3D image slices with intersliceresolutions lower than intraslice resolutions; a deep neural networkconfigured to upsample the first 3D image slices to produce second 3Dimage slices by extracting features in the first 3D image slices; acomputational framework for optimizing the deep neural networkconfigured to: downsample the second 3D image slices; calculate firstself-supervision losses by measuring differences between the downsampledimage slices and the first 3D image slices; and optimize the deep neuralnetwork to minimize the first self-supervision losses; and a userinterface for providing the third 3D image slices to a user.
 12. Thesystem of claim 11, wherein upsampling the first 3D image slicesproduces the second 3D image slices with increased intersliceresolutions.
 13. The system of claim 11, wherein the computationalframework is further configured to downsample the second 3D image slicesto dimensions of the first 3D image slices.
 14. The system of claim 11,wherein the computational framework is further configured to: switchdimensions of the second 3D image slices to produce third 3D imageslices; and downsample the third 3D image slices to dimensions of thefirst 3D image slices.
 15. The system of claim 14, wherein switchingdimensions of the second 3D image slices produces the third 3D imageslices with interslice resolutions matching the intraslice resolutionsof the first 3D images.
 16. The system of claim 11, wherein the deepneural network is further configured to upsample the downsampled third3D image slices to produce fourth 3D image slices by extracting featuresin the downsampled fourth 3D image slices.
 17. The system of claim 16,wherein upsampling the downsampled third 3D image slices produces thefourth 3D image slices with dimensions of the third 3D image slices. 18.The system of claim 14, wherein the computational framework is furtherconfigured to: calculate second self-supervision losses by measuringdifferences between the fourth and third 3D image slices; switchdimensions of the fourth 3D image slices to produce fifth 3D imageslices; downsample the fifth 3D image slices to produce sixth 3D imageslices; calculate third self supervision losses by measuring differencesbetween the sixth 3D image slices and the first 3D image slices; andoptimize the deep neural network to minimize the second and thirdself-supervision losses.
 19. The system of claim 18, wherein switchingdimensions of the fourth 3D image slices produces the fifth 3D imageslices with intraslice and interslice axes matching intraslice andinterslice axes of the first 3D image slices.
 20. The system of claim18, wherein downsampling the fifth 3D image slices produces the sixth 3Dimage slices with dimensions of the first 3D image slices.